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Results 11 - 20 of 58 for _input_ (0.1 sec)
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tensorflow/c/c_api.cc
void TF_AddInput(TF_OperationDescription* desc, TF_Output input) { desc->node_builder.Input(&input.oper->node, input.index); } void TF_AddInputList(TF_OperationDescription* desc, const TF_Output* inputs, int num_inputs) { std::vector<NodeBuilder::NodeOut> input_list; input_list.reserve(num_inputs); for (int i = 0; i < num_inputs; ++i) { input_list.emplace_back(&inputs[i].oper->node, inputs[i].index); }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Apr 15 03:35:10 UTC 2024 - 102.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/tfr/examples/mnist/ops_defs.py
] @Composite( 'NewFullyConnected', inputs=['input_: T', 'filter_: T', 'bias: T'], attrs=['act: {"", "RELU", "RELU6", "TANH"} = ""'], derived_attrs=['T: {float, int8}'], outputs=['o: T']) def _composite_fully_connected(input_, filter_, bias, act): res = tf.raw_ops.MatMul( a=input_, b=filter_, transpose_a=False, transpose_b=True) res = tf.raw_ops.Add(x=res, y=bias)
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Aug 31 20:23:51 UTC 2023 - 6.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/tfrt/tests/tf_to_corert/control_flow.mlir
} // CHECK-LABEL: func @tensor_array_while_test // CHECK-SAME: ([[in_chain:%.*]]: !tfrt.chain func.func @tensor_array_while_test(%indices: tensor<?xi32>, %input_0: tensor<?x?x?xf32>, %input_1: tensor<?x?x?xf32>) -> (tensor<?x?x512xf32>, tensor<?x?x512xf32>) { %index = "tf.Const"() {device = "/device:CPU:0", value = dense<0> : tensor<i32>} : () -> (tensor<i32>)
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 14 00:40:32 UTC 2024 - 17.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/tfr/examples/pad/pad_ops_test.py
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Sep 28 21:37:05 UTC 2021 - 3.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/tfr/examples/pad/ops_defs.py
num_split=2) input_ = tf.raw_ops.Concat( concat_dim=i, values=[left_padding, input_, right_padding]) return input_ @tf.RegisterGradient('NewMirrorPad') def _mirror_pad_grad(op, grad): mode = op.get_attr('mode') return [gen_array_ops.mirror_pad_grad(grad, op.inputs[1], mode=mode), None] @Composite( 'NewMirrorPadGrad', inputs=['input_: T', 'paddings: Tpaddings'],
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Oct 01 05:00:29 UTC 2021 - 5.6K bytes - Viewed (0) -
tensorflow/cc/framework/ops.h
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Sat Apr 13 05:57:22 UTC 2024 - 10.5K bytes - Viewed (0) -
tensorflow/c/experimental/gradients/math_grad.cc
class MatMulGradientFunction : public GradientFunction { public: explicit MatMulGradientFunction(vector<AbstractTensorHandle*> f_inputs, AttrBuilder f_attrs) : forward_inputs_(f_inputs), forward_attrs_(f_attrs) { for (auto input : forward_inputs_) { if (input) { input->Ref(); } } } Status Compute(AbstractContext* ctx,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Feb 28 13:53:47 UTC 2024 - 15.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/graphdef2mlir/switch_n.pbtxt
} } } node { name: "Case/branch0/_0/mul_0" op: "Mul" input: "Case/Case/input_0/_7" input: "Case/branch0/_0/mul/y" attr { key: "T" value { type: DT_FLOAT } } } node { name: "Case/branch1/_1/mul_0" op: "Mul" input: "Case/Case/input_0/_7:1" input: "Case/branch1/_1/mul/y" attr { key: "T" value { type: DT_FLOAT
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Nov 15 19:42:47 UTC 2021 - 3.7K bytes - Viewed (0) -
tensorflow/cc/framework/gradients.cc
return errors::InvalidArgument( "Must specify a gradient input for each output."); } std::vector<bool> reachable_nodes = GetReachableNodes(); for (const Output& input : inputs_) { if (!reachable_nodes[input.node()->id()]) { return errors::InvalidArgument( "Cannot compute the partial derivative for node '", input.node()->name(), "' as it's unreachable from the output node(s).");
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Sat Apr 13 05:57:22 UTC 2024 - 22K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/stablehlo/transforms/composite_avg_pool_patterns.td
(MHLO_CompositeOp:$old_val (variadic $a_input), ConstantStrAttr<StrAttr, "aten.avg_pool2d.default">, $attrs, $_, $_), (TFL_TransposeOp (TFL_AveragePool2DOp /*input*/ (TFL_TransposeOp $a_input, (Arith_ConstantOp
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 28 23:16:05 UTC 2024 - 7.8K bytes - Viewed (0)